Modeling Nonlinear Dynamics of Functionalization Layers: Enhancing Gas Sensor Sensitivity for Piezoelectrically Driven Microcantilever.

This article presents a parametrized response model that enhances the limit of detection (LOD) of piezoelectrically driven microcantilever (PD-MC) based gas sensors by accounting for the adsorption-induced variations in elastic properties of the functionalization layer (binder) and the nonlinear motional dynamics of the PD-MC. The developed model is demonstrated for quantifying cadaverine, a volatile biogenic diamine whose concentration is used to assess the freshness of meat. At low concentrations of cadaverine, an increase in the resonance frequency is observed, contrary to the expected reduction due to mass added by adsorption. The study explores the variations in the elastic modulus vis-à-vis the adsorbed mass of cadaverine and derives the resonance frequency to the adsorbed mass response function. We advance a blended technique involving the analysis of atomic force microscopy (AFM) force-distance (f-d) curves and fitting of the quartz crystal microbalance (QCM) impedance response spectrum to deduce the adsorption-induced changes in the viscoelastic properties of the functionalization layer. The findings obtained are subsequently employed in modeling the response function for a structurally nonhomogenous PD-MC, highlighting the significance of the functionalization layer to the global elastic properties. The structural composition of the PD-MC beam adopted herein features a trapezoidal base hosting the actuating piezoelectric stratum and a rectangular free end with a functionalization layer. The Euler-Bernoulli beam theory coupled with Hamilton's principle is used to develop the equation of motion, which is subsequently discretized into a set of nonlinear ordinary differential equations via Galerkin expansion, and the solutions to the first fundamental mode of vibration are determined using the method of multiple scales. The obtained solutions provide a basis for deducing the nonlinear response function model to the adsorbed mass. The derived model is validated by recorded resonance frequency changes resulting from exposure to known concentrations of cadaverine. We demonstrate that the increase in resonance frequency for low concentrations of cadaverine is due to the dominance of the variation of the elastic modulus of the functionalization layer originating from the initial binder-analyte interactions over damping due to added mass. It is concluded that the developed nonlinear response function model can reliably be used to quantify the cadaverine concentration at low concentrations with an elevated Limit of Detection.

Fast-Response and Low-Power Self-Heating Gas Sensor Using Metal/Metal Oxide/Metal (MMOM) Structured Nanowires.

With the escalating global awareness of air quality management, the need for continuous and reliable monitoring of toxic gases by using low-power operating systems has become increasingly important. One of which, semiconductor metal oxide gas sensors have received great attention due to their high/fast response and simple working mechanism. More specifically, self-heating metal oxide gas sensors, wherein direct thermal activation in the sensing material, have been sought for their low power-consuming characteristics. However, previous works have neglected to address the temperature distribution within the sensing material, resulting in inefficient gas response and prolonged response/recovery times, particularly due to the low-temperature regions. Here, we present a unique metal/metal oxide/metal (MMOM) nanowire architecture that conductively confines heat to the sensing material, achieving high uniformity in the temperature distribution. The proposed structure enables uniform thermal activation within the sensing material, allowing the sensor to efficiently react with the toxic gas. As a result, the proposed MMOM gas sensor showed significantly enhanced gas response (from 6.7 to 20.1% at 30 ppm), response time (from 195 to 17 s at 30 ppm), and limit of detection (∼1 ppm) when compared to those of conventional single-material structures upon exposure to carbon monoxide. Furthermore, the proposed work demonstrated low power consumption (2.36 mW) and high thermal durability (1500 on/off cycles), demonstrating its potential for practical applications in reliable and low-power operating gas sensor systems. These results propose a new paradigm for power-efficient and robust self-heating metal oxide gas sensors with potential implications for other fields requiring thermal engineering.

Thermodynamically and Kinetically Enhanced Benzene Vapor Sensor Based on the Cu-TCPP-Cu MOF with Extremely Low Limit of Detection.

As a carcinogenic and highly neurotoxic hazardous gas, benzene vapor is particularly difficult to be distinguished in BTEX (benzene, toluene, ethylbenzene, xylene) atmosphere and be detected in low concentrations due to its chemical inertness. Herein, we develop a depth-related pore structure in Cu-TCPP-Cu to thermodynamically and kinetically enhance the adsorption of benzene vapor and realize the detection of ultralow-temperature benzene gas. We find that the in-plane π electronic nature and proper pore sizes in Cu-TCPP-Cu can selectively induce the adsorption and diffusion of BTEX. Interestingly, the theoretical calculations (including density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations) exhibit that benzene molecules are preferred to adsorb and array as a consecutive arrangement mode in the Cu-TCPP-Cu pore, while the TEX (toluene, ethylbenzene, xylene) dominate the jumping arrangement model. The differences in distribution behaviors can allow adsorption and diffusion of more benzene molecules within limited room. Furthermore, the optimal pore-depth range (60-65 nm) of Cu-TCPP-Cu allows more exposure of active sites and hinders the gas-blocking process. The optimized sensor exhibits ultrahigh sensitivity to benzene vapor (155 Hz/µg@1 ppm), fast response time (less than 10 s), extremely low limit of detection (65 ppb), and excellent selectivity (83%). Our research thus provides a fundamental understanding to design and optimize two-dimensional metal-organic framework (MOF)-based gas sensors.

Chemiresistive Sensor for Enhanced CO2 Gas Monitoring.

Carbon dioxide (CO2) gas sensing and monitoring have gained prominence for applications such as smart food packaging, environmental monitoring of greenhouse gases, and medical diagnostic tests. Although CO2 sensors based on metal oxide semiconductors are readily available, they often suffer from limitations such as high operating temperatures (>250 °C), limited response at elevated humidity levels (>60% RH), bulkiness, and limited selectivity. In this study, we designed a chemiresistive sensor for CO2 detection to overcome these problems. The sensing material of this sensor consists of a CO2 switchable polymer based on N-3-(dimethylamino)propyl methacrylamide (DMAPMAm) and methoxyethyl methacrylate (MEMA) [P(D-co-M)], and diethylamine. The designed sensor has a detection range for CO2 between 103 and 106 ppm even at high humidity levels (>80% RH), and it is capable of differentiating ammonia at low concentrations (0.1-5 ppm) from CO2. The addition of diethylamine improved sensor performance such as selectivity, response/recovery time, and long-term stability. These data demonstrate the potential of using this sensor for the detection of food spoilage.

MXene/Hydrogel-based bioelectronic nose for the direct evaluation of food spoilage in both liquid and gas-phase environments.

Various bioelectronic noses have been recently developed for mimicking human olfactory systems. However, achieving direct monitoring of gas-phase molecules remains a challenge for the development of bioelectronic noses due to the instability of receptor and the limitations of its surrounding microenvironment. Here, we report a MXene/hydrogel-based bioelectronic nose for the sensitive detection of liquid and gaseous hexanal, a signature odorant from spoiled food. In this study, a conducting MXene/hydrogel structure was formed on a sensor via physical adsorption. Then, canine olfactory receptor 5269-embedded nanodiscs (cfOR5269NDs) which could selectively recognize hexanal molecules were embedded in the three-dimensional (3D) MXene/hydrogel structures using glutaraldehyde as a linker. Our MXene/hydrogel-based bioelectronic nose exhibited a high selectivity and sensitivity for monitoring hexanal in both liquid and gas phases. The bioelectronic noses could sensitively detect liquid and gaseous hexanal down to 10-18 M and 6.9 ppm, and they had wide detection ranges of 10-18 - 10-6 M and 6.9-32.9 ppm, respectively. Moreover, our bioelectronic nose allowed us to monitor hexanal levels in fish and milk. In this respect, our MXene/hydrogel-based bioelectronic nose could be a practical strategy for versatile applications such as food spoilage assessments in both liquid and gaseous systems.

Up-bottom assessments of nutrient supply and gaseous pollutant from Chinese wheat straw field management.

To achieve resource efficiency, and carbon neutrality, it is vital to evaluate nutrient supply and gaseous pollutant emissions associated with field management of bio-straw resources. Previous straw yield estimates have typically relied on a constant grain-to-straw yield ratio without accounting for grain yield levels in a given region. Addressing this high-resolution data gap, our study introduces a novel empirical model for quantifying grain-to-straw yield, which has been used to gauge wheat straw field management practices at the city level during 2011-2015. Utilizing both statistical review and GIS-based methods, average nitrogen (N), phosphorus (P), and potassium (K) supplies from straw field management stood at 1510, 1229, and 61700 tons, respectively. Average emissions of PM2.5, SO2, NOx, NH3, CH4, and CO2 due to straw burning were 367, 41, 160, 18, 165, and 70,644 tons, respectively. We also reported uncertainty from Monte Carlo model as the 5th-95th percentiles of estimated nutrient supply and gaseous pollutant. These insights will provide foundational support for the sustainable and environmentally friendly management of wheat straw in China.

High-Performance Ni3(HHTP)2 Film-Based Flexible Field-Effect Transistor Gas Sensors.

Conductive metal-organic frameworks (MOFs) have received increasing attention in recent years and present high application potential as sensing elements in electronic sensors. In this study, flexible field-effect transistor (FET) sensors based on conductive MOF, i.e., Ni3(HHTP)2, have been constructed. This Ni3(HHTP)2 sensor has high sensitivity (detection limit of 56 ppb) as well as superior selectivity for NO2 detection at room temperature, which is demonstrated by accurate gas detection in a mixed gas atmosphere. Moreover, by employing six flexible substrates, i.e., polyimide (PI), tape (PET), facemask, paper cup, tablecloth, and take-out bag (textile), we successfully demonstrate the universality of the flexible sensor construction with conductive MOF as sensing film on various substrates. This study of conductive MOF-based flexible electronic sensors offers a new opportunity for a wide range of sensing applications with wearable and portable electronic devices.

The Challenges and Opportunities for TiO2 Nanostructures in Gas Sensing.

Chemiresistive gas sensors based on metal oxides have been widely applied in industrial monitoring, medical diagnosis, environmental pollutant detection, and food safety. To further enhance the gas sensing performance, researchers have worked to modify the structure and function of the material so that it can adapt to different gas types and environmental conditions. Among the numerous gas-sensitive materials, n-type TiO2 semiconductors are a focus of attention for their high stability, excellent biosafety, controllable carrier concentration, and low manufacturing cost. This Perspective first introduces the sensing mechanism of TiO2 nanostructures and composite TiO2-based nanomaterials and then analyzes the relationship between their gas-sensitive properties and their structure and composition, focusing also on technical issues such as doping, heterojunctions, and functional applications. The applications and challenges of TiO2-based nanostructured gas sensors in food safety, medical diagnosis, environmental detection, and other fields are also summarized in detail. Finally, in the context of their practical application challenges, future development technologies and new sensing concepts are explored, providing new ideas and directions for the development of multifunctional intelligent gas sensors in various application fields.

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation.

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.

Confinement Enrichment Effect in HoMS-BaTiO3 Microwave Gas Sensors for the Detection of 10 ppb-0.55 v/v% Ammonia at Room Temperature.

The construction of ammonia gas sensors with wide detection ranges is important for exhalation diagnosis and environmental pollution monitoring. To achieve a wide detection range, sensitive materials must possess excellent spatial confinement and large active surfaces to enhance gas adsorption. In this study, an ammonia microwave gas sensor with a wide detection range of 10 ppb-0.55 v/v% at room temperature was fabricated by incorporating hollow multishelled-structured BaTiO3 (HoMS-BaTiO3). The effect of the number of shells and the quantity of the sensitive material on the gas-sensing performance was investigated, and two-layered HoMS-BaTiO3 demonstrated the best response at high concentrations (0.15-0.55 v/v%). Conversely, single-layered HoMS-BaTiO3 displayed outstanding performance at low concentrations (10 ppb-0.15 v/v%). The lower the quantity of the sensitive material, the higher the response. This study offers a method for preparing room-temperature ammonia sensors with a wide detection range and reveals the link between the structure and quantity of sensitive materials and gas-sensing performance.

Bangkok school indoor air quality: monitoring and intervention by positive pressure fresh air system.

A PM2.5 crisis in Thailand has caused the Thai government and public to be increasingly concerned about children's exposure to PM2.5 during time in school. This study is a part of a project to create a modeled effective school indoor air quality management for the Bangkok Metropolitan Administration (BMA). We measured air quality and environment in 10 Bangkok school rooms, including CO2, CO, O3, PM2.5, PM10, TVOCPID, formaldehyde, airborne bacteria and fungi, and gaseous organic contaminants. The indoor-to-outdoor concentration ratios indicated that either outdoor sources or indoor + outdoor sources were the predominant contributors to PM in naturally ventilated classrooms. Meanwhile, PM levels in air-conditioned classrooms strongly depended on class activities. CO2 measurements showed that the air-conditioned classrooms had a low 0.4 per hour air change rate and total fungal counts also reached 800 CFU m-3. Analysis of gaseous organic compounds showed that the two most abundant were aliphatic and aromatic hydrocarbons, accounting for 60% by mass concentration. Interestingly, 2-ethyl-1-hexanol, a mucous membrane irritant, was detected in all study rooms. In one naturally ventilated classroom, we implemented a positive pressure fresh air system to mitigate in-class PM levels; it kept PM levels below 20 µg m-3 throughout the class day. Students reported a 20-37% increase in satisfaction with the perceived indoor environmental quality and reported reduced rates in all symptoms of the sick building syndrome after implementing the positive pressure system.

Correlation analysis and predicting modeling of pyrolysis gas based on landfill excavated waste pyrolysis characteristics.

The contribution of excavated waste to waste management is multifaceted, including minimization, non-hazardous disposal, access to useable land resources, improved waste management techniques and public environmental awareness, consistent with recent circular economy initiatives. Pyrolysis can be converted into tar, pyrolysis gas and char with recyclable utilization, enriching the application of pyrolysis technology in the field of excavation waste. In this study, the pyrolysis system includes horizontal tube furnace, gas collection device and Micro GC. The excavated waste was pyrolyzed at a temperature of 500∼900 °C with a heating rate of 10 °C/min. Pyrolysis gases include H2, CO, CO2, CH4, C2H4, C2H6 and C3H8. Pyrolysis was divided into four stages, the main decomposition range is 230∼500 °C, with a weight loss rate of 68.49% and a co-pyrolysis behavior. As the temperature increases, the tar and char decreased and the gas production increased significantly, and the pyrolysis gas reached 47.02% at 900 °C. According to Pearson correlation coefficient analysis, the generation of H2 and CO is positively correlated with temperature. Therefore, the target products can be influenced by changing the parameters, when considering the practical utilization of the excavated waste pyrolysis products. On this basis, the prediction models were built by polynomial fitting method. This model can reduce the experimental exploration cycle, reduce the cost, and accurately predict the pyrolysis gas, which has practical guidance for the application of pyrolysis industry, and provides a theoretical basis for the resource recycling and energy recovery of landfill.

A chromatogram decomposition method and its associated calibration for quantitative analysis.

We present a new chromatogram decomposition method for Gas Chromatography (GC) which represents a chromatogram as a sum of template functions inspired by the analytic solution of mass balance equation. The proposed method starts by approximating GC response of a single gas by these template functions. Consequently, it utilizes the temporal translation and dilation of this approximate response to approximate GC responses of other gases of interest. The results are demonstrated on lab data using calibration bottles containing mixtures of C1-C5. Correlation of the amplitudes of the decomposed responses and injected concentrations indicates linear calibration curves are sufficient to estimate C1-C5 concentrations. The performance of the method is demonstrated by a ratio test where a calibration bottle with C1 concentration 300 times larger than C2 and C3 concentrations is injected into GC.

The effects of gases from food waste on human health: A systematic review.

Food waste is a routine and increasingly growing global concern that has drawn significant attention from policymakers, climate change activists and health practitioners. Amid the plurality of discourses on food waste-health linkages, however, the health risks from food waste induced emissions have remained under explored. This lack of evidence is partly because of the lack of complete understanding of the effects of food waste emissions from household food waste on human health either directly through physiological mechanisms or indirectly through environmental exposure effects. Thus, this systematic review contributes to the literature by synthesizing available evidence to highlight gaps and offers a comprehensive baseline inventory of food waste emissions and their associated impacts on human health to support public health decision-making. Four database searches: Web of Science, OVID(Medline), EMBASE, and Scopus, were searched from inception to 3 May 2023. Pairs of reviewers screened 2189 potentially eligible studies that addressed food waste emissions from consumers and how the emissions related to human health. Following PRISMA guidelines, 26 articles were eligible for data extraction for the systematic review. Findings indicate that emissions from food waste, such as hydrogen sulphide, ammonia, and volatile organic carbons, can affect human endocrine, respiratory, nervous, and olfactory systems. The severity of the human health effects depends on the gaseous concentration, but range from mild lung irritation to cancer and death. This study recommends emission capture technologies, food diversion programs, and biogas technologies to reduce food waste emissions.

Landfill bacteriology: Role in waste bioprocessing elevated landfill gaseselimination and heat management.

This study delves into the critical role of microbial ecosystems in landfills, which are pivotal for handling municipal solid waste (MSW). Within these landfills, a complex interplay of several microorganisms (aerobic/anaerobic bacteria, archaea or methanotrophs), drives the conversion of complex substrates into simplified compounds and complete mineralization into the water, inorganic salts, and gases, including biofuel methane gas. These landfills have dominant biotic and abiotic environments where various bacterial, archaeal, and fungal groups evolve and interact to decompose substrate by enabling hydrolytic, fermentative, and methanogenic processes. Each landfill consists of diverse bio-geochemical environments with complex microbial populations, ranging from deeply underground anaerobic methanogenic systems to near-surface aerobic systems. These kinds of landfill generate leachates which in turn emerged as a significant risk to the surrounding because generated leachates are rich in toxic organic/inorganic components, heavy metals, minerals, ammonia and xenobiotics. In addition to this, microbial communities in a landfill ecosystem could not be accurately identified using lab microbial-culturing methods alone because most of the landfill's microorganisms cannot grow on a culture medium. Due to these reasons, research on landfills microbiome has flourished which has been characterized by a change from a culture-dependent approach to a more sophisticated use of molecular techniques like Sanger Sequencing and Next-Generation Sequencing (NGS). These sequencing techniques have completely revolutionized the identification and analysis of these diverse microbial communities. This review underscores the significance of microbial functions in waste decomposition, gas management, and heat control in landfills. It further explores how modern sequencing technologies have transformed our approach to studying these complex ecosystems, offering deeper insights into their taxonomic composition and functionality.

Field demonstration for the solvent-based sampling method to perform compound-specific isotope analysis on gas-phase VOC.

The solvent-based sampling method for collecting gas-phase volatile organic compounds (VOCs) and conducting compound-specific isotope analysis (CSIA) was deployed during a controlled field study. The solvent-based method used methanol as a sink to accumulate petroleum hydrocarbons during the sampling of soil air and effluent gas. For each gaseous sample collected, carbon isotope analysis (δ13C) was conducted for a selection of five VOCs (benzene, toluene, o-xylene, cyclopentane and octane) emitted by a synthetic hydrocarbon source emplaced in the subsurface. The δ13C values obtained for gaseous VOCs (collected from soil gas and effluent gas) were compared to measurements obtained for the same VOCs present in the source material (none aqueous phase liquid - NAPL) and dissolved in groundwater to evaluate the reliability of the solvent-based sampling method in providing accurate isotope measurements. Since the NAPL source was composed of only 12 VOCs, potential bias related to the analytical procedure (such as co-elution) were avoided, hence emphasizing on field-related bias. This field evaluation demonstrated the capacity of the solvent-based method to produce precise and accurate δ13C measurements. The isotopic discrepancies between the gaseous and the NAPL values were < 1 ‰ for 39 out of the 41 comparison points, thus deemed not statistically different based on a common isotopic uncertainty error of ±0.5 ‰. Moreover, the current field study is the first field study to report δ13C measurements for up to five gas-phase VOCs obtained from the same sample, which appears to be of interest for VOC fate or forensic studies. The possibility to use several VOC isotopic measurements enabled by the sampling method would contribute to strengthen the connection assessment between gaseous VOCs and the suspected emitting source. Accordingly, the field results presented herein support the application of this sampling methodology to conduct CSIA assessment in the frame of VOC vapor studies.

Atmospheric mercury uptake and accumulation in forests dependent on climatic factors.

The environmental and climatic factors dictating atmospheric mercury (Hg) uptake by foliage and accumulation within the forest floor are evaluated across six mountain sites, South Korea, using Hg concentration and Hg stable isotope analyses. The isotope ratios of total gaseous Hg (TGM) at six mountains are explained by local anthropogenic Hg emission influence and partly by mountain elevation and wind speed. The extent to which TGM is taken up by foliage is not dependent on the site-specific TGM concentration, but by the local wind speed, which facilitates TGM passage through dense deciduous canopies in the Korean forests. This is depicted by the significant positive relationship between wind speed and foliage Hg concentration (r2 = 0.92, p < 0.05) and the magnitude of δ202Hg shift from TGM to foliage (r2 = 0.37, p > 0.05), associated with TGM uptake and oxidation by foliar tissues. The litter and topsoil Hg concentrations and isotope ratios reveal relationships with a wide range of factors, revealing lower Hg level and greater isotopic fractionation at sites with low elevation, high wind speed, and high mean warmest temperature. We attribute this phenomenon to active TGM re-emission from the forest floor at sites with high wind speed and high temperature, caused by turnover of labile organic matter and decomposition. In contrast to prior studies, we observe no significant effect of precipitation on forest Hg accumulation but precipitation appears to reduce foliage-level Hg uptake by scavenging atmospheric Hg species available for stomata uptake. The results of this study would enable better prediction of future atmospheric and forest Hg influence under climate change.

Revisiting the Ultraviolet Absorption Cross Section of Gaseous Nitrous Acid (HONO): New Insights for Atmospheric HONO Budget.

Nitrous acid (HONO) is an important source of hydroxyl radicals (OH) in the atmosphere. Precise determination of the absolute ultraviolet (UV) absorption cross section of gaseous HONO lays the basis for the accurate measurement of its concentration by optical methods and the estimation of HONO loss rate through photolysis. In this study, we performed a series of laboratory and field intercomparison experiments for HONO measurement between striping coil-liquid waveguide capillary cell (SC-LWCC) photometry and incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS). Specified HONO concentrations prepared by an ultrapure standard HONO source were utilized for laboratory intercomparisons. Results show a consistent ∼22% negative bias in measurements of the IBBCEAS compared with a SC-LWCC photometer. It is confirmed that the discrepancies occurring between these techniques are associated with the overestimation of the absolute UV absorption cross sections through careful analysis of possible uncertainties. We quantified the absorption cross section of gaseous HONO (360-390 nm) utilizing a custom-built IBBCEAS instrument, and the results were found to be 22-34% lower than the previously published absorption cross sections widely used in HONO concentration retrieval and atmospheric chemical transport models (CTMs). This suggests that the HONO concentrations retrieved by optical methods based on absolute absorption cross sections may have been underestimated by over 20%. Plus, the daytime loss rate and unidentified sources of HONO may also have evidently been overestimated in pre-existing studies. In summary, our findings underscore the significance of revisiting the absolute absorption cross section of HONO and the re-evaluation of the previously reported HONO budgets.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks.

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001-12.41% MAPE, 0.001-5.82 mg Nm-3 MAE, and 0.03-52.30 mg Nm-3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10-6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

Assessment of the association between health problems and cooking fuel type, and barriers towards clean cooking among rural household people in Bangladesh.

BACKGROUND: In low- and middle-income countries, households mainly use solid fuels like wood, charcoal, dung, agricultural residues, and coal for cooking. This poses significant public health concerns due to the emission of harmful particles and gases. To address these issues and support Sustainable Development Goals (SDGs), adopting cleaner cooking fuels like electricity and gas are acknowledged as a viable solution. However, access to these cleaner fuels is limited, especially in rural areas. METHODS: This study conducted a face-to-face survey with 1240 individuals in rural Bangladesh to explore the link between health issues and cooking fuel type, as well as barriers to transitioning to clean cooking. Using a convenient sampling technique across four divisions/regions, the survey gathered socio-demographic and health data, along with information on clean cooking barriers through a semi-structured questionnaire. Binary and multivariable logistic regression analyses were then employed to identify significant associations between cooking fuel type and health problems. RESULTS: The study revealed that a majority of participants (73.3%) relied on solid fuel for cooking. The use of solid fuel was significantly correlated with factors such as lower education levels, reduced family income, location of residence, and the experience of health issues such as cough, chest pressure while breathing, eye discomfort, diabetes, asthma, and allergies. Economic challenges emerged as the foremost obstacle to the adoption of clean cooking, accompanied by other contributing factors. CONCLUSION: The use of solid fuel in rural Bangladeshi households poses substantial health risks, correlating with respiratory, eye, cardiovascular, and metabolic issues. Lower education and income levels, along with specific residential locations, were associated with higher solid fuel usage. Economic challenges emerged as the primary obstacle to adopting clean cooking practices. These findings emphasize the need for implementing strategies to promote clean cooking, address barriers, and contribute to achieving Sustainable Development Goal targets for health and sustainable energy access in Bangladesh.